Observational study identifies clinical and biochemical factors linked to menstrual dysfunction in junior athletes, indicating future research directions.
OBJECTIVE: Investigate the risk factors for menstrual dysfunction in junior athletes. DESIGN: Observational case-control study. SETTING: Consecutive junior female athletes with menstrual dysfunction who visited the Shinshu University Hospital enrolled in this study. The control group comprised normal healthy athletes during a routine first-year health checkup at the Matsusho Gakuen High School. PARTICIPANTS: A total of 61 female junior athletes were analyzed in this study, including 21 in the menstrual dysfunction group (median age: 16.0 years) and 40 in the control group (median age: 15.6 years). ASSESSMENT OF RISK FACTORS: Information on anthropometric parameters, exercise behavior, lifestyle, and blood data of the study cohort was compared with that of normal healthy adolescent athletes as a control group. Logistic regression analysis was conducted to identify the risk factors for menstrual dysfunction in junior athletes. MAIN OUTCOME MEASURES: Menstrual dysfunction was defined as primary or secondary amenorrhea, excluding cases due to organic diseases. RESULTS: Multivariate analysis revealed that white blood cell count, blood urea nitrogen, free triiodothyronine, zinc, and estradiol were all significantly associated with the presence of menstrual dysfunction in junior female athletes after adjusting for age and percentage of standard weight (all P < 0.017 with Bonferroni correction). Exercise energy expenditure and sleep time were also weakly related to menstrual dysfunction. However, substantial overlap in laboratory values was observed between groups. CONCLUSIONS: Several clinical and biochemical variables were associated with menstrual dysfunction in this exploratory analysis. These findings do not establish clinically applicable thresholds or screening utility but may inform future prospective studies aimed at clarifying pathophysiological mechanisms and potential risk prediction models in adolescent athletes.
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Morota et al. (2026) studied this question.
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